{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.  , 0.05, 0.1 , 0.15, 0.2 , 0.25, 0.3 , 0.35, 0.4 , 0.45, 0.5 ,\n",
       "       0.55, 0.6 , 0.65, 0.7 , 0.75, 0.8 , 0.85, 0.9 , 0.95, 1.  ])"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "x_known = np.array([0.194, 0.429, 0.512, 0.821, 0.917])\n",
    "\n",
    "y_known = np.array([0.347, 0.543, 0.661, 0.811, 0.793])\n",
    "\n",
    "x_desired = np.linspace(0, 1, num=21)\n",
    "\n",
    "x_desired"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_desired = np.interp(x_desired, x_known, y_known)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.194, 0.429, 0.512, 0.821, 0.917, 0.0, 0.05, 0.1, 0.15000000000000002, 0.2, 0.25, 0.30000000000000004, 0.35000000000000003, 0.4, 0.45, 0.5, 0.55, 0.6000000000000001, 0.65, 0.7000000000000001, 0.75, 0.8, 0.8500000000000001, 0.9, 0.9500000000000001, 1.0]\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 将NumPy数组拼接在一起\n",
    "x = list(x_known)\n",
    "x.extend(list(x_desired))\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.347, 0.543, 0.661, 0.811, 0.793, 0.347, 0.347, 0.347, 0.347, 0.3520042553191489, 0.3937063829787234, 0.4354085106382979, 0.47711063829787237, 0.5188127659574469, 0.5728554216867471, 0.6439397590361446, 0.6794466019417477, 0.7037184466019418, 0.727990291262136, 0.7522621359223302, 0.7765339805825243, 0.8008058252427186, 0.8055625000000001, 0.7961875, 0.793, 0.793]\n"
     ]
    }
   ],
   "source": [
    "y = list(y_known)\n",
    "y.extend(list(y_desired))\n",
    "print(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘图\n",
    "plt.plot(x_known, y_known)\n",
    "plt.plot(x_desired, y_desired)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
